Eye-Path and Memory-Prediction Framework

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Table of Contents

Introduction
Project Background
Reference List

Introduction

Online marketing and advertising actively develop nowadays, and modern advertisers need to focus on the customers’ attitudes and behaviours in the context of the effectiveness of the advertisement’s location on the web page.

Thus, it is important to answer the questions on how customers look at advertisements, focus their attention, and how their eye movements are motivated in order to understand how the location or the character of online information can affect the user’s attitude and behaviour.

The problem is in the fact that according to Jeff Hawkins’s memory-prediction framework, people are inclined to create a memory model and match memory patterns of known objects and, as a result, predict future activities. However, when there is the prediction mismatch, a man’s cognitive activities change, affecting his attention (Hawkins 2005). Therefore, the effects of the memory-prediction framework are important for influencing the customers’ focus in advertising.

Project Background

The study of eye tracking or eye movements for predicting the effectiveness of online advertisements is actively used by researchers during recent years (Michailidou 2014, p. 670; Ratwani & Trafton 2011).

However, the discussion of Hawkins’s memory-prediction framework in the spheres of marketing and advertising is limited. Furthermore, there is the lack of discussions related to the role of the prediction mismatch for eye tracking and further customers’ attitudes.

Project Aims and Objectives

The aim of the project is to investigate the use of the prediction mismatch in order to capture the user’s attention in online advertising.

The objectives of the project are the following ones:

In cooperation with Think Eye Tracking, eye tracking or eye movements of website users will be explored and fixed.
Eye tracking will be analysed to determine possible prediction mismatch.
Changes in the users’ attention, focus, and attitude will be determined and analysed.

Intellectual Challenge

The investigation in the sphere of online advertising that uses the Hawkins’s memory-prediction framework as the theoretical background is academically challenging because it utilises the specific idea about the prediction mismatch in order to discuss how users’ attention and attitudes can be reflected in eye tracking and influence their attitude.

The research allows the development of the theory on the effective use of the prediction mismatch and web location for increasing the effectiveness of advertisements.

Research Approach/Programme

The researcher will use the eye tracking technologies for analyzing the users’ activities on the books online website. Two pages of the same website will be proposed to users for examination. The pages will be different in the amount of presented information on them.

The eye movement of users while reading web pages will be analysed in detail to understand which page is more attractive for them and on which locations the users focus on. The changes in the users’ focuses and attention will be analysed along with the possible changes in attitudes to the information and product.

Deliverables

Identified changes in users’ eye movements and focuses will demonstrate how expectations associated with the prediction mismatch influence the users’ examination of the web page; what parts of the web pages become more attractive for the customers; and effects of the prediction mismatch on the user’s attention.

Resources

The required resources are the eye tracking technologies provided by Think Eye Tracking for working with the books online store website (Think eye tracking 2014).

Reference List

Hawkins, J 2005, On intelligence, Henry Holt and Co., New York.

Michailidou, E 2014, ‘Towards predicting ad effectiveness via an eye tracking study’, Lecture Notes in Computer Science, vol. 8527, no. 1, pp. 670-680.

Ratwani, R & Trafton, G 2011, ‘A real-time eye tracking system for predicting and preventing postcompletion errors’, Human–Computer Interaction, vol. 26, no. 3, pp. 205-245.

Think eye tracking 2014. Web.

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